Orthogonal frequency division multiplexing (OFDM) is a signal modulation technique in which a transmitter divides a signal, and then transmits the divided signal over several subcarriers. The subcarriers are located on a frequency axis at regular intervals. With the OFDM technique, in contrast with conventional serial communication techniques, the transmitted signal is divided into N streams, and the N stream are then transmitted in parallel over N subcarriers, each having a separate carrier frequency. The OFDM technique transmits the signal reliably and efficiently at a high data rate.
The subcarriers are made “orthogonal” by appropriately selecting the spacing of the frequencies in the frequency band. Therefore, spectral overlapping among subcarriers is allowed because the orthogonality ensures that the receiver can separate the OFDM subcarriers. With OFDM, a better spectral efficiency is achieved than by using a simple frequency division multiplexing technique. OFDM is more robust to data loss due to multipath fading when compared with a single carrier because OFDM has an increased symbol period for the same aggregate data rate.
In addition, inter-symbol interference (ISI) in OFDM transmissions can be prevented by inserting a guard interval before each transmitted block of symbols. Moreover, OFDM is robust to frequency selective fading because each sub-channel occupies a relatively narrow frequency band, where the characteristic of the channel frequency is relatively flat. Thus, OFDM is used by many communication systems, including digital audio and video broadcasting (DAB, DVB), and high-speed digital subscriber line (DSL) modems over a twisted pair of wires. OFDM can also be used in wireless local area networks (WLANs), and fixed broadband wireless communication networks.
However, it is not possible to make reliable data decisions in OFDM systems unless a good channel estimate is available. Thus, an efficient and accurate channel estimation method is necessary to coherently demodulate received data. Although differential detection could be used to detect the transmitted signals in the absence of channel information, this would result in about a 3 dB loss in SNR compared to coherent detection.
A number of channel estimation techniques are known in the prior art. In most of those, the channel estimates are continuously updated by transmitting pilot symbols using specified time-frequency lattices. One class of such pilot assisted estimation processes adopt an interpolation technique with fixed one or two-dimensional parameters to estimate the frequency domain channel impulse response (CIR). Channel estimates are obtained at lattices assigned to the pilot tones, see Jae Kyoung Moon and Song In Choi, “Performance of channel estimation methods for OFDM systems in multipath fading channels,” IEEE Transactions on Consumer Electronics, Vol. 46, No. 1, February 2000, pp. 161–170, P. Hoeher, S. Kaiser, and P. Robertson, “Two-dimensional pilot-symbol-sided channel estimation by Wiener filtering,” Proceedings of 1997 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP-97, vol. 3, pp. 1845–1848, and F. Said and A. H. Aghvami, “Linear two dimensional pilot assisted channel estimation for OFDM systems,” 6th IEEE Conference on Telecommunications, 1998, pp. 32–36. Linear, spline and Gaussian filters can all be used with these methods.
Another class of methods adopt known channel statistics and channel estimates at pilot symbols to estimate the CIR in the sense of minimum mean square error (MMSE), see Ye Li, Leonard J. Cimini, Jr., and Nelson R. Sollenberger, “Robust channel estimation for OFDM systems with rapid dispersive fading channels,” IEEE Transactions on Communication, Vol. 46, No. 7, July 1998, pp. 902–915, J.-J. van de Beek, O. Edfors, M. Sandell, S. K. Wilson, and P. O. Borjesson, “On channel estimation in OFDM systems,” IEEE Vehicular Technology Conference, 1995, Vol. 2, pp. 815–819, and O. Edfors, M. Sandell, S. K. Wilson, J.-J. van de Beek, and P. O. Borjesson, “OFDM channel estimation by singular value decomposition,” IEEE Transactions on Communications, Vol. 46 No. 7, July 1998, pp. 931–939. Shortcomings of those processes include a large error floor that may be incurred by a mismatch between the estimated and real CIRs, and difficulty in obtaining the correlation function of the channel impulse response.
Therefore, there is a need for an efficient method for estimating channels so that received symbols can be coherently detected and demodulated.